docstr: Inverse transform samples from the warped space back to the original space
  
  Parameters
  ----------
  data_t: List
      Samples to inverse transform. Should be of shape
      (<# samples>, :attr:`transformed_n_dims`)
  
  Returns
  -------
  List
      Samples transformed back to the original space. Will be of shape
      (<# samples>, :attr:`n_dims`)
docstr_tok: ["Inverse","transform","samples","from","the","warped","space","back","to","the","original","space","Parameters","--","--","--","--","--","data_t",":","List","Samples","to","inverse","transform",".","Should","be","of","shape","(","<","#","samples",">",",",":","attr",":","`","transformed_n_dims","`",")","Returns","--","--","--","-","List","Samples","transformed","back","to","the","original","space",".","Will","be","of","shape","(","<","#","samples",">",",",":","attr",":","`","n_dims","`",")"]
code: def inverse_transform(self, data_t):
      """Inverse transform samples from the warped space back to the original space
  
      Parameters
      ----------
      data_t: List
          Samples to inverse transform. Should be of shape
          (<# samples>, :attr:`transformed_n_dims`)
  
      Returns
      -------
      List
          Samples transformed back to the original space. Will be of shape
          (<# samples>, :attr:`n_dims`)"""
      #################### Inverse Transform ####################
      columns = []
      start = 0
  
      for j in range(self.n_dims):
          dim = self.dimensions[j]
          offset = dim.transformed_size
  
          if offset == 1:
              columns.append(dim.inverse_transform(data_t[:, start]))
          else:
              columns.append(
                  dim.inverse_transform(
                      data_t[:, start : start + offset]
                  )
              )
  
          start += offset
  
      #################### Transpose ####################
      rows = []
      # TODO: Use `np.transpose`? Might that screw up the dimension types (mostly `Categorical`)
      for i in range(len(data_t)):
          r = []
          for j in range(self.n_dims):
              r.append(columns[j][i])
  
          rows.append(r)
  
      return rows
  
imports: from hyperparameter_hunter.space.dimensions import Dimension, Real, Integer, Categorical
  from hyperparameter_hunter.utils.general_utils import short_repr
  from functools import reduce
  import numbers
  import numpy
  from sys import maxsize
  from sklearn.utils import check_random_state
  from sklearn.utils.fixes import sp_version
label_desc: Use image node_id 9 for calling the Space obj's underlying member method code with example usage: obj.inverse_transform(data_t) and returns: rows